Over the past year, questions about AI adoption have started appearing in almost every recovery audit conversation.
In the past six months alone, I’ve heard some version of the same question in nearly every client discussion: “How is your recovery audit firm currently leveraging AI, and what is the potential?”
It shows up in RFPs, it is asked for in presentations and increasingly becomes the focus of webinars and social media discussions about finance technology.
AI is a meaningful tool in recovery audits. It scans large datasets and quickly identifies patterns within that data — transactions that look like duplicates, unusual credit activity, or payment behaviors that fall outside typical ranges. That capability is valuable. It allows auditors to review more information, faster, and prioritize where to focus their attention. It reduces the time needed to perform a recovery audit by at least 25%.
AI is excellent at finding patterns. Auditors are responsible for proving them.
A flagged transaction still must answer two practical questions:
Is it really an error?
Is it recoverable?
Answering those questions requires context and judgment.
For example, an AI model might flag two invoices as potential duplicates because they share the same vendor, amount, and invoice number.
An experienced auditor might see something else entirely. The vendor split a shipment across two locations and used the same invoice number on both invoices. Both payments are legitimate.
Without context, the algorithm identifies a problem that doesn’t exist.
Supplier contracts, pricing adjustments, rebates, freight terms, and settlement agreements all affect how transactions appear in raw data. What looks unusual in a dataset may make perfect sense once documentation and operational context are reviewed.
This is where experienced auditors will continue to play a critical role. They can distinguish between a true duplicate and a legitimate transaction, when an open credit is collectible and when it will never clear, and recognize when an anomaly points to a larger process gap worth investigating and addressing. They understand that identifying a potential error is very different from recovering it.
Recovering funds requires validation, documentation, and communication with both suppliers and internal AP teams. That’s the part vendors and AP teams ultimately rely on. This phase is most successful when handled with a combination of experience, finesse, and most importantly, human connection.
AI reduces the manual work and allows auditors to focus on what drives results. The result isn’t fewer auditors. It’s better outcomes.
